To reduce the public health threat from bio-terrorism, various government agencies, including the Centers for Disease Control and Prevention (CDC), have issued guidelines for health care professionals about recognizing illnesses that might be associated with intentional release of biologic agents. Although these guidelines are helpful, many diseases with recognized bio-terrorism potential (e.g., smallpox) mimic relatively common illnesses such as influenza, and can be difficult to detect at an early stage even with increased knowledge and awareness.
Another approach is to require doctors to fill out “disease templates” when the physician treats “suspicious” patients. However, this does not satisfy the need for early detection, as subtle patterns can escape detection by the physician unless seen in a larger context. Moreover, individual physicians and hospitals may not be able to rapidly detect unusual clusters of acute illness. A cluster of related symptoms in a small geographic region could signal an early outbreak. However, each case individually might look relatively benign.
Furthermore, the concept of filling out a “suspicious patient template” is fundamentally flawed. If the doctor has suspicion about a patient, then there are a many other ways to confirm that the patient is the victim of bio-terrorism. It is the patient who does not raise a red flag until it is too late that we are most concerned about.
It is worth reflecting that for at least two of the anthrax patients who died, part of the problem was an initial misdiagnosis. For a virulent disease such as smallpox, which may be even harder to diagnose, detecting the disease a day or two earlier (some studies have suggested that even hours may make a big difference) might prevent or at least control its spread.
Attempts have been made to analyze available hospital admissions information to rapidly detect an attack. For example, an artificial intelligence system called the Real-time Outbreak Detection System (RODS), developed jointly by the University of Pittsburgh and Carnegie-Mellon University, examines hospital admissions records for hidden patterns. Although RODS is promising in some respects, results are hampered by the use of admissions information. Better results could be obtained if the entire patient record was consulted. For instance, during the recent anthrax attacks, no spike in admissions of an unusual nature took place, and only clinical information could have revealed an outbreak of anthrax.
Currently, clinical information is stored in a myriad of structured and unstructured data sources. It may be necessary to access numerous different databases, each with its own peculiar format. Worse, physician notes may have to be consulted. These notes usually are nothing more than free text dictations, and it may be very difficult to sift through the notes to gather the necessary information. Yet only unstructured data may reveal important indications of an unusual disease outbreak. (At least if we hope to detect it before there are sufficient cases that lead to an increase in admissions).
Given the importance of early detection of unusual disease incidents, it would be desirable and highly advantageous to provide new techniques for automatically identifying disease outbreak to reduce the threat from bio-terrorism.